Matteo Spatola | Searcharxiv

arXiv2025

Fault Tolerant Reconfigurable ML Multiprocessor

This paper reports three computational experiments for a von Neumann inspired reconfigurable fault tolerant multiprocessor for neural network (NN) training workflows. The experiments are intended to prove the feasibility of the proposed reconfigurable multiprocessor architecture for non-regular workflows on robustness of adaptability. A potential integration with MLIR compilers is also discussed for integrating diverse accelerator hardware for existing practical applications.

cs.NI↗

arXiv2025

PCRLLM: Proof-Carrying Reasoning with Large Language Models under Stepwise Logical Constraints

Large Language Models (LLMs) often exhibit limited logical coherence, mapping premises to conclusions without adherence to explicit inference rules. We propose Proof-Carrying Reasoning with LLMs (PCRLLM), a framework that constrains reasoning to single-step inferences while preserving natural language formulations. Each output explicitly specifies premises, rules, and conclusions, thereby enabling verification against a target logic. This mechanism mitigates trustworthiness concerns by supporting chain-level validation even in black-box settings. Moreover, PCRLLM facilitates systematic multi-LLM collaboration, allowing intermediate steps to be compared and integrated under formal rules. Finally, we introduce a benchmark schema for generating large-scale step-level reasoning data, combining natural language expressiveness with formal rigor.

cs.CL↗